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Found 22 papers, 9 papers with code
Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering
Our approach includes a density Mip-VoG for scene geometry and a feature Mip-VoG with a small MLP for view-dependent color.
NeuDA: Neural Deformable Anchor for High-Fidelity Implicit Surface Reconstruction
This paper studies implicit surface reconstruction leveraging differentiable ray casting.
3D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer Avenue
However, a drawback is that the synthesized views through Neural Fields Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR pipelines, especially when object interactions in the 3D scene creation cause local shadows.
Ray Priors through Reprojection: Improving Neural Radiance Fields for Novel View Extrapolation
Neural Radiance Fields (NeRF) have emerged as a potent paradigm for representing scenes and synthesizing photo-realistic images.
Modeling Indirect Illumination for Inverse Rendering
The key insight is that indirect illumination can be conveniently derived from the neural radiance field learned from input images instead of being estimated jointly with direct illumination and materials.
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint
Unsupervised image-to-image (I2I) translation aims to learn a domain mapping function that can preserve the semantics of the input images without paired data.
Minimal Geometry-Distortion Constraint for Unsupervised Image-to-Image Translation
Unsupervised image-to-image (I2I) translation, which aims to learn a domain mapping function without paired data, is very challenging because the function is highly under-constrained.
Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation
Adapting semantic segmentation models to new domains is an important but challenging problem.
Domain Generalization via Entropy Regularization
To arrive at this, some methods introduce a domain discriminator through adversarial learning to match the feature distributions in multiple source domains.
Ranked #43 on
Domain Generalization
on PACS
3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics
Currently, 3D-FRONT contains 18, 968 rooms diversely furnished by 3D objects, far surpassing all publicly available scene datasets.
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database.
3D-FUTURE: 3D Furniture shape with TextURE
The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories.
3D Object Reconstruction From A Single Image
3D Shape Retrieval
+4
Adaptive Context-Aware Multi-Modal Network for Depth Completion
Furthermore, considering the mutli-modality of input data, we exploit the graph propagation on the two modalities respectively to extract multi-modal representations.
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach
Due to the stylish difference between synthetic and real images, we propose a temporally-consistent domain adaptation (TCDA) approach that simultaneously explores labels in the synthetic domain and temporal constraints in the videos to improve style transfer and depth prediction.
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
Supervised depth estimation has achieved high accuracy due to the advanced deep network architectures.
Ranked #68 on
Monocular Depth Estimation
on KITTI Eigen split
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping
Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples.
MoE-SPNet: A Mixture-of-Experts Scene Parsing Network
In the proposed networks, different levels of features at each spatial location are adaptively re-weighted according to the local structure and surrounding contextual information before aggregation.
Deep Ordinal Regression Network for Monocular Depth Estimation
These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions.
Ranked #13 on
Depth Estimation
on NYU-Depth V2
A Compromise Principle in Deep Monocular Depth Estimation
However, we find that training a network to predict a high spatial resolution continuous depth map often suffers from poor local solutions.
Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks
The human visual system excels at detecting local blur of visual images, but the underlying mechanism is not well understood.
Occlusion Boundary Detection via Deep Exploration of Context
Occlusion boundaries contain rich perceptual information about the underlying scene structure.
